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A Conceptual Model for Uncertainty Demand Forecasting by Artificial Neural Network and Adaptive Neuro - Fuzzy Inference System Based on Quantitative and Qualitative Data

Premporn Khemavuk & Athiwat Leenatham

International Journal of Operations and Quantitative Management2021https://doi.org/10.46970/2021.26.4.3article
AJG 1ABDC C
Weight
0.39

What the paper says

The purpose of this research is to present the new concepts for demand forecasting using artificial intelligence methods. In the first part, it demonstrates the evolution of demand forecasting from the past using traditional forecasting methods to the present using artificial intelligence forecasting methods. ANN and ANFIS were presented in this study with quantitative and qualitative data. The structure construction of the model is described to create various models in both the single forecasting method and the combined forecasting method to gain the best accuracy. There are two research questions as follows. 1. Are proposed methods with qualitative data more accurate than the one without qualitative data? 2. Is combined method forecast more accurate than single method forecast?

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https://doi.org/https://doi.org/10.46970/2021.26.4.3

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@article{premporn2021,
  title        = {{A Conceptual Model for Uncertainty Demand Forecasting by Artificial Neural Network and Adaptive Neuro - Fuzzy Inference System Based on Quantitative and Qualitative Data}},
  author       = {Premporn Khemavuk & Athiwat Leenatham},
  journal      = {International Journal of Operations and Quantitative Management},
  year         = {2021},
  doi          = {https://doi.org/https://doi.org/10.46970/2021.26.4.3},
}

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Evidence weight

0.39

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.10 × 0.4 = 0.04
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.